Semantic Representation Based on Deep Learning for Spam Detection

[1]  Kranti Ghag,et al.  Word Embedding Based Multinomial Naive Bayes Algorithm for Spam Filtering , 2018, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).

[2]  Kamel Adi,et al.  A Supervised Approach for Spam Detection Using Text-Based Semantic Representation , 2017, MCETECH.

[3]  Jun Zhang,et al.  Twitter spam detection based on deep learning , 2017, ACSW.

[4]  José María Gómez Hidalgo,et al.  Does Sentiment Analysis Help in Bayesian Spam Filtering? , 2016, HAIS.

[5]  Peng Wang,et al.  Semantic Clustering and Convolutional Neural Network for Short Text Categorization , 2015, ACL.

[6]  P Visalakshi.  Latent Semantic Indexing Based SVM Model for Email Spam Classification , 2014 .

[7]  Jian Pei,et al.  Email mining: tasks, common techniques, and tools , 2013, Knowledge and Information Systems.

[8]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[9]  Gonzalo Álvarez,et al.  Word sense disambiguation for spam filtering , 2012, Electron. Commer. Res. Appl..

[10]  Igor Santos,et al.  Enhanced Topic-based Vector Space Model for semantics-aware spam filtering , 2012, Expert Syst. Appl..

[11]  Maozhen Li,et al.  A survey of emerging approaches to spam filtering , 2012, CSUR.

[12]  Jácint Szabó,et al.  Latent dirichlet allocation in web spam filtering , 2008, AIRWeb '08.

[13]  Artem Polyvyanyy,et al.  A Quantitative Evalution of the Enhanced Topic-based Vector Space Model , 2007 .

[14]  Peter A. Flach,et al.  Subgroup Discovery with CN2-SD , 2004, J. Mach. Learn. Res..

[15]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.